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Structural transitions in liquid semiconductor alloys: A molecular dynamics study with a neural network potential
Yi-Bin Fang1,2, Cheng Shang2,3, Zhi-Pan Liu2,3
1Key Laboratory for Computational Physical Sciences (MOE), State Key Laboratory of Surface Physics, Department of Physics, Fudan University, Shanghai 200433, China.
The Journal of Chemical Physics
|September 11, 2024
Summary
Neural network potentials enable molecular dynamics simulations of liquid CdTe, CdS, and alloys. Simulations reveal pressure-induced structural transitions and temperature-dependent atomic arrangements, offering insights into condensed matter physics.
Area of Science:
- Condensed matter physics
- Materials science
Background:
- Liquid-liquid phase transitions are crucial but computationally challenging.
- Neural network (NN) potentials offer a solution for simulating complex liquid systems.
Purpose of the Study:
- Investigate structural transitions in liquid CdTe, CdS, and their alloys under varying pressure and temperature.
- Utilize the LaspNN potential for accurate molecular dynamics simulations.
Main Methods:
- Employed molecular dynamics simulations powered by the LaspNN neural network potential.
- Analyzed structural changes in liquid CdTe, CdS, and CdSxTe1-x alloys across different pressures and temperatures.
Main Results:
- Identified three pressure-dependent liquid structures (tetrahedral, rock salt, close-packed) resembling solid states.
- Observed Te chain and S dimer formation at high temperatures, detailing atomic arrangements.
- Characterized distinct structural transitions in CdSxTe1-x alloys based on S/Te substitution ratios.
Conclusions:
- Developed a comprehensive temperature-pressure phase diagram for liquid CdSxTe1-x alloys.
- Established linear and nonlinear relationships between alloy composition and local aggregation of Te and S atoms, respectively.
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